Near-Range Camera Assembly for Vehicles

ABSTRACT

A near-range camera assembly for a vehicle is provided. The near-range camera assembly includes a LIDAR mount couplable to a vehicle. The LIDAR mount includes a first surface and a second surface that opposes the first surface. The near-range camera assembly includes a LIDAR unit removably coupled to the first surface LIDAR mount. The near-range camera assembly further includes a camera and a camera housing for the camera. The camera housing is removably coupled to the second surface LIDAR mount.

RELATED APPLICATION

The present application is based on and claims benefit of U.S.Provisional Patent Application No. 63/104,125 having a filing date ofOct. 22, 2020, which is incorporated by reference herein.

FIELD

The present disclosure relates generally to vehicles and, moreparticularly, a sensor platform for mounting a light detection andranging (Lidar) unit and a camera to an autonomous vehicle.

BACKGROUND

An autonomous vehicle can be capable of sensing its environment andnavigating with little to no human input. In particular, an autonomousvehicle can observe its surrounding environment using a variety ofsensors and can attempt to comprehend the environment by performingvarious processing techniques on data collected by the sensors. Givensuch knowledge, an autonomous vehicle can navigate through theenvironment.

SUMMARY

Aspects and advantages of embodiments of the present disclosure will beset forth in part in the following description, or may be learned fromthe description, or may be learned through practice of the embodiments.

In one aspect, a mounting assembly for a near-range camera assemblycomprising a LIDAR unit and a camera is provided. The mounting assemblyincludes a LIDAR mount couplable to an autonomous vehicle. The LIDARmount includes a first surface and a second surface that is opposite thefirst surface. The mounting assembly further includes a camera housingremovably coupled to the LIDAR mount. When the LIDAR unit is coupled tothe LIDAR mount, the LIDAR unit is coupled to the first surface of theLIDAR mount. Conversely, when the camera housing is coupled to the LIDARmount, the camera housing is coupled to the second surface of the LIDARmount.

In another aspect, a near-range camera assembly is provided. Thenear-range camera assembly includes a LIDAR mount couplable to anautonomous vehicle. The LIDAR mount includes a first surface and asecond surface that is opposite the first surface. The near-range cameraassembly includes a LIDAR unit removably coupled to the first surface ofthe LIDAR mount. The near-range camera assembly includes a camera and acamera housing. The camera housing is removably coupled to the secondsurface of the LIDAR mount.

In yet another aspect, an autonomous vehicle is provided. The autonomousvehicle includes a vehicle body and a plurality of near-range cameraassemblies. Each of the near-range camera assemblies is coupled to thevehicle body at a different location. Each of the plurality ofnear-range camera assemblies include a LIDAR mount couplable to thevehicle body. The LIDAR mount includes a first surface and a secondsurface that is opposite the first surface. Each of the plurality ofnear-range camera assemblies includes a LIDAR unit removably coupled tothe first surface of the LIDAR mount. Each of the plurality ofnear-range camera assemblies further includes a camera and a camerahousing for the camera. The camera housing is removably coupled to thesecond surface of the LIDAR mount.

The autonomous vehicle technology described herein can help improve thesafety of passengers of an autonomous vehicle, improve the safety of thesurroundings of the autonomous vehicle, improve the experience of therider and/or operator of the autonomous vehicle, as well as provideother improvements as described herein. Moreover, the autonomous vehicletechnology of the present disclosure can help improve the ability of anautonomous vehicle to effectively provide vehicle services to others andsupport the various members of the community in which the autonomousvehicle is operating, including persons with reduced mobility and/orpersons that are underserved by other transportation options.Additionally, the autonomous vehicle of the present disclosure mayreduce traffic congestion in communities as well as provide alternateforms of transportation that may provide environmental benefits.

These and other features, aspects and advantages of various embodimentswill become better understood with reference to the followingdescription and appended claims. The accompanying drawings, which areincorporated in and constitute a part of this specification, illustrateembodiments of the present disclosure and, together with thedescription, serve to explain the related principles.

BRIEF DESCRIPTION OF THE DRAWINGS

Detailed discussion of embodiments directed to one of ordinary skill inthe art are set forth in the specification, which makes reference to theappended figures, in which:

FIG. 1 depicts a block diagram of an example system for controlling anautonomous vehicle according to example embodiments of the presentdisclosure.

FIG. 2 depicts a top view of an autonomous vehicle having a plurality ofnear-range camera assemblies located at different locations thereonaccording to example embodiments of the present disclosure.

FIG. 3 depicts a near-range camera assembly mounted to a grille of anautonomous vehicle according to example embodiments of the presentdisclosure.

FIG. 4 depicts a near-range camera assembly according to exampleembodiments of the present disclosure

FIG. 5 depicts a top perspective view of a mounting assembly for anear-range camera assembly according to example embodiments of thepresent disclosure.

FIG. 6 depicts a bottom perspective view of the mounting assembly ofFIG. 5 according to example embodiments of the present disclosure.

FIG. 7 depicts a side view of the mounting assembly of FIG. 5 accordingto example embodiments of the present disclosure.

FIG. 8 depicts a cover of a mounting assembly for a near-range cameraassembly according to example embodiments of the present disclosure.

FIG. 9 depicts a cutaway view of a portion of the near-range cameraassembly according to example embodiments of the present disclosure.

FIG. 10 depicts another near-range camera assembly according to exampleembodiments of the present disclosure.

FIG. 11 depicts a block diagram of components of a LIDAR unit accordingto example embodiments of the present disclosure.

DETAILED DESCRIPTION

Example aspect of the present disclosure are directed to a near-rangecamera assembly for vehicles. The near-range camera assembly can includea LIDAR unit and a camera. The LIDAR unit can include a housing. TheLIDAR unit can further include one or more emitters disposed within thehousing and one or more receivers disposed within the housing. The oneor more emitters can include a laser source configured to emit a laserbeam. The one or more receivers can include a detector (e.g.,photodetector) configured to detect a reflected laser beam. In someimplementations, multiple near-range camera assemblies can be removablymounted to a vehicle body of the vehicle. Furthermore, in suchimplementations, each of the near-range camera assemblies can beremovably coupled to the vehicle body at a different location (e.g.,front bumper, rear bumper, door frame).

The near-range camera assembly can include a sensor platform or mountingassembly to facilitate coupling the near-range camera assembly to avehicle. The mounting assembly can include a LIDAR mount for the LIDARunit. The LIDAR mount can be couplable to a vehicle body (e.g., frontbumper, rear bumper, door frame, etc.) of the vehicle. For instance, insome implementations, the LIDAR mount can be directly coupled to thevehicle body. In alternative implementations, the LIDAR mount can becoupled to the vehicle body via one or more brackets.

The mounting assembly can further include a camera mount or camerahousing for the camera. The camera housing can be removably coupled toLIDAR mount at a different location than the LIDAR unit. For instance,the LIDAR unit can be removably coupled to a first surface of the LIDARmount. In some implementations, the LIDAR unit can contact the firstsurface of the LIDAR mount when the LIDAR unit is coupled thereto.Conversely, the camera housing can be removably coupled to a secondsurface of the LIDAR mount. For instance, in some implementations, thefirst surface of the LIDAR mount and the second surface of the LIDARmount can correspond to opposing surfaces of the LIDAR mount. In someimplementations, a portion of the camera housing can contact the secondsurface of the LIDAR mount when the camera housing is coupled thereto.

In some implementations, the camera housing can be removably coupled tothe LIDAR mount via one or more fasteners that are separate from one ormore fasteners used to couple the LIDAR unit to the LIDAR mount. In thismanner, the LIDAR unit and the camera housing can be independentlycoupled to the LIDAR mount. This allows technicians to remove the LIDARunit to perform maintenance thereon without also removing the camerahousing. Likewise, technicians can remove the camera housing to performa maintenance action on the camera without also removing the LIDAR unit.

The camera housing can be removably coupled to the LIDAR mount such thatthe camera is oriented at an acute angle relative to the LIDAR mountwhen the camera is positioned within the camera housing. In this manner,a field of view of the camera can include a larger portion of objects(e.g., pedestrian, bicycle) in close proximity (e.g., less than 10meters, less than 5 meters, etc.) to the autonomous vehicle. Forinstance, in some implementations, the acute angle can be less thanabout 20 degrees. Furthermore, in some implementations, the field ofview of the camera can intersect a field of view of the LIDAR unit due,at least in part, to the camera being tilted at an acute angle relativeto the LIDAR mount.

In some implementations, the mounting assembly can include a cover thatis removably coupled to the LIDAR mount. The cover can define a firstopening and a second opening. The LIDAR unit can be positioned withinthe first opening when the cover is coupled to the LIDAR mount.Furthermore, a portion (e.g., lens) of the camera can be positionedwithin the second opening when the cover is coupled to the LIDAR mount.As such, the first opening can be larger than the second opening. Thecover can protect the mounting assembly (e.g., LIDAR mount, camerahousing) from debris.

In some implementations, the mounting assembly can include a retentionplate positioned to retain the camera within the camera housing.Furthermore, in some implementations, the mounting assembly can includea spring plate positioned between the retention plate and the camerahousing such that the retention plate exerts a force on the camera viathe spring plate to retain the camera within the housing. Morespecifically, the force can restrict movement of the camera relative tothe camera housing such that the lens of the camera remains positionedwithin an opening defined by the camera housing.

In some implementations, debris can be cleaned from the LIDAR unit andthe camera via one or more cleaning systems. For instance, the one ormore cleaning systems can include a first cleaning system and a secondcleaning system. The first cleaning system can be configured to cleandebris from the housing of the LIDAR unit. The second cleaning systemcan be configured to clean debris from the lens of the camera. In someimplementations, the first cleaning system and/or the second cleaningsystem can include an air knife system.

In some implementations, the first cleaning system and the secondcleaning system can operate concurrently. In this manner, debris can becleaned from the housing of the LIDAR unit and the lens of the camera atthe same time. In alternative implementations, the first cleaning systemand the second cleaning system can be operated at different times. Forinstance, in some implementations, the first cleaning system can beoperated to clean debris from the housing of the LIDAR unit when a speedof the vehicle is greater than a first threshold value (e.g., 5 MPH) andless than a second threshold value (e.g., 20 MPH). Conversely, thesecond cleaning system can be operated when the speed of the vehicle isless than the first threshold value. For instance, the second cleaningsystem can be operated to clean debris from the lens of the camera whenthe autonomous vehicle is idle (e.g., not moving) at an intersection.Furthermore, in some implementations, operation of the first cleaningsystem and the second cleaning system can be prohibited when the speedof the autonomous vehicle is above the second threshold value. Forexample, operation of the first cleaning system and the second cleaningsystem when the autonomous vehicle is traversing a highway orinterstate.

An autonomous vehicle can perform vehicle services for one or moreservice entities. A service entity can be associated with the provisionof one or more vehicle services. For example, a service entity can be anindividual, a group of individuals, a company (e.g., a business entity,organization, etc.), a group of entities (e.g., affiliated companies),and/or another type of entity that offers and/or coordinates theprovision of vehicle service(s) to one or more users. As an example, aservice entity can offer vehicle service(s) to users via a softwareapplication (e.g., on a user computing device), via a website, and/orvia other types of interfaces that allow a user to request a vehicleservice. The vehicle services can include user transportation services(e.g., by which the vehicle transports user(s) from one location toanother), delivery services (e.g., by which a vehicle delivers item(s)to a requested destination location), courier services (e.g., by which avehicle retrieves item(s) from a requested origin location and deliversthe item to a requested destination location), and/or other types ofservices.

An operations computing system of the service entity can help tocoordinate the performance of vehicle services by autonomous vehicles.For instance, the operations computing system can include a serviceplatform. The service platform can include a plurality of back-endservices and front-end interfaces, which are accessible via one or moreAPIs. For example, an autonomous vehicle and/or another computing systemthat is remote from the autonomous vehicle can communicate/access theservice platform (and its backend services) by calling the one or moreAPIs. Such components can facilitate secure, bidirectionalcommunications between autonomous vehicles and/or the service entity'soperations system (e.g., including a data center, etc.).

The service platform can allow an autonomous vehicle to obtain data fromand/or communicate data to the operations computing system. By way ofexample, a user can provide (e.g., via a user device) a request for avehicle service to the operations computing system associated with theservice entity. The request can indicate the type of vehicle servicethat the user desires (e.g., a user transportation service, a deliveryservice, etc.), one or more locations (e.g., an origin, destination,etc.), timing constraints (e.g., pick-up time, drop-off time, deadlines,etc.), a number of user(s) and/or items to be transported in thevehicle, other service parameters (e.g., a need for handicap access,handle with care instructions, etc.), and/or other information. Theoperations computing system of the service entity can process therequest and identify one or more autonomous vehicles that may be able toperform the requested vehicle services for the user. For instance, theoperations computing system can identify which autonomous vehicle(s) areonline with the service entity (e.g., available for a vehicle serviceassignment, addressing a vehicle service assignment, etc.). Anautonomous vehicle can go online with a service entity by, for example,connecting with the service entity's operations computing system (e.g.,the service platform) so that the vehicle computing system cancommunicate with the operations computing system via a network. Onceonline, the operations computing system can communicate a vehicleservice assignment indicative of the requested vehicle services and/orother data to the autonomous vehicle.

In some implementations, the near-range camera assembly can beimplemented on an autonomous vehicle. The autonomous vehicle (e.g.,ground-based vehicle, aerial vehicle, light electric vehicle, etc.) caninclude various systems and devices configured to control the operationof the vehicle. For example, an autonomous vehicle can include anonboard vehicle computing system (e.g., located on or within theautonomous vehicle) that is configured to operate the autonomousvehicle. The vehicle computing system can obtain sensor data fromsensor(s) onboard the vehicle (e.g., cameras, LIDAR, RADAR, etc.),attempt to comprehend the vehicle's surrounding environment byperforming various processing techniques on the sensor data, andgenerate an appropriate motion plan through the vehicle's surroundingenvironment. Moreover, an autonomous vehicle can include acommunications system that can allow the vehicle to communicate with acomputing system that is remote from the vehicle such as, for example,that of a service entity.

The autonomous vehicle's sensor(s) can be configured to acquire sensordata. The sensor(s) can be external sensors configured to acquireexternal sensor data. This can include sensor data associated with thesurrounding environment of the vehicle. The surrounding environment ofthe vehicle can include/be represented in the field of view of thesensor(s). For instance, the sensor(s) can acquire image and/or otherdata of the environment outside of the vehicle and within a range and/orfield of view of one or more of the sensor(s). The sensor(s) can includeone or more Light Detection and Ranging (LIDAR) systems, one or moreRadio Detection and Ranging (RADAR) systems, one or more cameras (e.g.,visible spectrum cameras, infrared cameras, etc.), one or more motionsensors, one or more audio sensors (e.g., microphones, etc.), and/orother types of imaging capture devices and/or sensors. The sensor datacan include image data (e.g., 2D camera data, video data, etc.), RADARdata, LIDAR data (e.g., 3D point cloud data, etc.), audio data, and/orother types of data. The one or more sensors can be located on variousparts of the vehicle including a front side, rear side, left side, rightside, top, and/or bottom of the vehicle. As described herein, thesensor(s) can be mounted to the exterior of the autonomous vehicle.

In some implementations, the sensor(s) can include one or more internalsensors. The internal sensor(s) can be configured to acquire sensor dataassociated with the interior of the autonomous vehicle. For example, theinternal sensor(s) can include one or more cameras (e.g., cabincameras), one or more infrared sensors, one or more motion sensors, oneor more weight sensors (e.g., in a seat, in a trunk, etc.), and/or othertypes of sensors. The sensor data acquired via the internal sensor(s)can include, for example, image data indicative of a position of apassenger or item located within the interior (e.g., cabin, trunk, etc.)of the vehicle. This information can be provided to a remote computingsystem (e.g., of the service entity) and used, for example, to ensurethe safety/comfort of the passenger, to prevent an item from being leftby a passenger, confirm the cleanliness of the vehicle, remotely assista passenger, confirm that special accommodations are available and metfor a passenger (e.g., wheelchair is positioned correctly and secured),etc.

The mounting assembly for the near-range camera assembly can providenumerous technical effects and benefits. For instance, the LIDAR unitand the camera can be serviced independent of one another, because theLIDAR unit and the camera housing are independently coupled to the LIDARmount. Furthermore, since the LIDAR unit is coupled to the LIDAR mountat a fixed location (e.g., first surface thereof), the LIDAR unit doesnot need to be recalibrated each time the LIDAR unit is recoupled to theLIDAR mount after being serviced. Likewise, since the camera housing iscoupled to the LIDAR mount at a fixed location (e.g., second surfacethereof), the camera does not need to be recalibrated each time thecamera housing is recoupled to the LIDAR mount. Still further, since thecamera housing is removably coupled to the LIDAR mount such that thecamera is positioned at an acute angle relative to the LIDAR mount, thecamera can be oriented such that a field of view thereof includes alarger portion of objects (e.g., pedestrian, bicyclist) in closeproximity to the autonomous vehicle. In this manner, classification ofobjects depicted in images obtained via the camera can be improved sincea larger portion of the objects is included in the field of view of thecamera due, at least in part, to the camera being oriented at an acuteangle relative to the LIDAR mount.

Referring now to the FIGS., FIG. 1 depicts a block diagram of an examplesystem 100 for controlling and communicating with a vehicle according toexample aspects of the present disclosure. As illustrated, FIG. 1 showsa system 100 that can include a vehicle 105 and a vehicle computingsystem 110 associated with the vehicle 105. The vehicle computing system110 can be located onboard the vehicle 105 (e.g., it can be included onand/or within the vehicle 105).

The vehicle 105 incorporating the vehicle computing system 110 can bevarious types of vehicles. For instance, the vehicle 105 can be anautonomous vehicle. The vehicle 105 can be a ground-based autonomousvehicle (e.g., car, truck, bus, etc.). The vehicle 105 can be anair-based autonomous vehicle (e.g., airplane, helicopter, verticaltake-off and lift (VTOL) aircraft, etc.). The vehicle 105 can be a lightweight elective vehicle (e.g., bicycle, scooter, etc.). The vehicle 105can be another type of vehicles (e.g., watercraft, etc.). The vehicle105 can drive, navigate, operate, etc. with minimal and/or nointeraction from a human operator (e.g., driver, pilot, etc.). In someimplementations, a human operator can be omitted from the vehicle 105(and/or also omitted from remote control of the vehicle 105). In someimplementations, a human operator can be included in the vehicle 105.

The vehicle 105 can be configured to operate in a plurality of operatingmodes. The vehicle 105 can be configured to operate in a fullyautonomous (e.g., self-driving) operating mode in which the vehicle 105is controllable without user input (e.g., can drive and navigate with noinput from a human operator present in the vehicle 105 and/or remotefrom the vehicle 105). The vehicle 105 can operate in a semi-autonomousoperating mode in which the vehicle 105 can operate with some input froma human operator present in the vehicle 105 (and/or a human operatorthat is remote from the vehicle 105). The vehicle 105 can enter into amanual operating mode in which the vehicle 105 is fully controllable bya human operator (e.g., human driver, pilot, etc.) and can be prohibitedand/or disabled (e.g., temporary, permanently, etc.) from performingautonomous navigation (e.g., autonomous driving, flying, etc.). Thevehicle 105 can be configured to operate in other modes such as, forexample, park and/or sleep modes (e.g., for use between tasks/actionssuch as waiting to provide a vehicle service, recharging, etc.). In someimplementations, the vehicle 105 can implement vehicle operatingassistance technology (e.g., collision mitigation system, power assiststeering, etc.), for example, to help assist the human operator of thevehicle 105 (e.g., while in a manual mode, etc.).

To help maintain and switch between operating modes, the vehiclecomputing system 110 can store data indicative of the operating modes ofthe vehicle 105 in a memory onboard the vehicle 105. For example, theoperating modes can be defined by an operating mode data structure(e.g., rule, list, table, etc.) that indicates one or more operatingparameters for the vehicle 105, while in the particular operating mode.For example, an operating mode data structure can indicate that thevehicle 105 is to autonomously plan its motion when in the fullyautonomous operating mode. The vehicle computing system 110 can accessthe memory when implementing an operating mode.

The operating mode of the vehicle 105 can be adjusted in a variety ofmanners. For example, the operating mode of the vehicle 105 can beselected remotely, off-board the vehicle 105. For example, a remotecomputing system (e.g., of a vehicle provider and/or service entityassociated with the vehicle 105) can communicate data to the vehicle 105instructing the vehicle 105 to enter into, exit from, maintain, etc. anoperating mode. By way of example, such data can instruct the vehicle105 to enter into the fully autonomous operating mode.

In some implementations, the operating mode of the vehicle 105 can beset onboard and/or near the vehicle 105. For example, the vehiclecomputing system 110 can automatically determine when and where thevehicle 105 is to enter, change, maintain, etc. a particular operatingmode (e.g., without user input). Additionally, or alternatively, theoperating mode of the vehicle 105 can be manually selected via one ormore interfaces located onboard the vehicle 105 (e.g., key switch,button, etc.) and/or associated with a computing device proximate to thevehicle 105 (e.g., a tablet operated by authorized personnel locatednear the vehicle 105). In some implementations, the operating mode ofthe vehicle 105 can be adjusted by manipulating a series of interfacesin a particular order to cause the vehicle 105 to enter into aparticular operating mode.

The vehicle computing system 110 can include one or more computingdevices located onboard the vehicle 105. For example, the computingdevice(s) can be located on and/or within the vehicle 105. The computingdevice(s) can include various components for performing variousoperations and functions. For instance, the computing device(s) caninclude one or more processors and one or more tangible, non-transitory,computer readable media (e.g., memory devices, etc.). The one or moretangible, non-transitory, computer readable media can store instructionsthat when executed by the one or more processors cause the vehicle 105(e.g., its computing system, one or more processors, etc.) to performoperations and functions, such as those described herein for controllingan autonomous vehicle, communicating with other computing systems, etc.

The vehicle 105 can include a communications system 115 configured toallow the vehicle computing system 110 (and its computing device(s)) tocommunicate with other computing devices. The communications system 115can include any suitable components for interfacing with one or morenetwork(s) 120, including, for example, transmitters, receivers, ports,controllers, antennas, and/or other suitable components that can helpfacilitate communication. In some implementations, the communicationssystem 115 can include a plurality of components (e.g., antennas,transmitters, and/or receivers) that allow it to implement and utilizemultiple-input, multiple-output (MIMO) technology and communicationtechniques.

The vehicle computing system 110 can use the communications system 115to communicate with one or more computing device(s) that are remote fromthe vehicle 105 over one or more networks 120 (e.g., via one or morewireless signal connections). The network(s) 120 can exchange (send orreceive) signals (e.g., electronic signals), data (e.g., data from acomputing device), and/or other information and include any combinationof various wired (e.g., twisted pair cable) and/or wirelesscommunication mechanisms (e.g., cellular, wireless, satellite,microwave, and radio frequency) and/or any desired network topology (ortopologies). For example, the network(s) 120 can include a local areanetwork (e.g. intranet), wide area network (e.g. Internet), wireless LANnetwork (e.g., via Wi-Fi), cellular network, a SATCOM network, VHFnetwork, a HF network, a WiMAX based network, and/or any other suitablecommunication network (or combination thereof) for transmitting data toand/or from the vehicle 105 and/or among computing systems.

In some implementations, the communications system 115 can also beconfigured to enable the vehicle 105 to communicate with and/or provideand/or receive data and/or signals from a remote computing deviceassociated with a user 125 and/or an item (e.g., an item to be picked-upfor a courier service). For example, the communications system 115 canallow the vehicle 105 to locate and/or exchange communications with auser device 130 of a user 125. In some implementations, thecommunications system 115 can allow communication among one or more ofthe system(s) on-board the vehicle 105.

As shown in FIG. 1, the vehicle 105 can include one or more sensors 135,an autonomy computing system 140, a vehicle interface 145, one or morevehicle control systems 150, and other systems, as described herein. Oneor more of these systems can be configured to communicate with oneanother via one or more communication channels. The communicationchannel(s) can include one or more data buses (e.g., controller areanetwork (CAN)), on-board diagnostics connector (e.g., OBD-II), and/or acombination of wired and/or wireless communication links. The onboardsystems can send and/or receive data, messages, signals, etc. amongstone another via the communication channel(s).

The sensor(s) 135 can be configured to acquire sensor data 155. Thesensor(s) 135 can be external sensors configured to acquire externalsensor data. This can include sensor data associated with thesurrounding environment of the vehicle 105. The surrounding environmentof the vehicle 105 can include/be represented in the field of view ofthe sensor(s) 135. For instance, the sensor(s) 135 can acquire imageand/or other data of the environment outside of the vehicle 105 andwithin a range and/or field of view of one or more of the sensor(s) 135.The sensor(s) 135 can include one or more Light Detection and Ranging(LIDAR) systems, one or more Radio Detection and Ranging (RADAR)systems, one or more cameras (e.g., visible spectrum cameras, infraredcameras, etc.), one or more motion sensors, one or more audio sensors(e.g., microphones, etc.), and/or other types of imaging capture devicesand/or sensors. The one or more sensors can be located on various partsof the vehicle 105 including a front side, rear side, left side, rightside, top, and/or bottom of the vehicle 105. The sensor data 155 caninclude image data (e.g., 2D camera data, video data, etc.), RADAR data,LIDAR data (e.g., 3D point cloud data, etc.), audio data, and/or othertypes of data. The vehicle 105 can also include other sensors configuredto acquire data associated with the vehicle 105. For example, thevehicle 105 can include inertial measurement unit(s), wheel odometrydevices, and/or other sensors.

In some implementations, the sensor(s) 135 can include one or moreinternal sensors. The internal sensor(s) can be configured to acquiresensor data 155 associated with the interior of the vehicle 105. Forexample, the internal sensor(s) can include one or more cameras, one ormore infrared sensors, one or more motion sensors, one or more weightsensors (e.g., in a seat, in a trunk, etc.), and/or other types ofsensors. The sensor data 155 acquired via the internal sensor(s) caninclude, for example, image data indicative of a position of a passengeror item located within the interior (e.g., cabin, trunk, etc.) of thevehicle 105. This information can be used, for example, to ensure thesafety of the passenger, to prevent an item from being left by apassenger, confirm the cleanliness of the vehicle 105, remotely assist apassenger, etc.

In some implementations, the sensor data 155 can be indicative of one ormore objects within the surrounding environment of the vehicle 105. Theobject(s) can include, for example, vehicles, pedestrians, bicycles,and/or other objects. The object(s) can be located in front of, to therear of, to the side of, above, below the vehicle 105, etc. The sensordata 155 can be indicative of locations associated with the object(s)within the surrounding environment of the vehicle 105 at one or moretimes. The object(s) can be static objects (e.g., not in motion) and/ordynamic objects/actors (e.g., in motion or likely to be in motion) inthe vehicle's environment. The sensor(s) 135 can provide the sensor data155 to the autonomy computing system 140.

In addition to the sensor data 155, the autonomy computing system 140can obtain map data 160. The map data 160 can provide detailedinformation about the surrounding environment of the vehicle 105 and/orthe geographic area in which the vehicle was, is, and/or will belocated. For example, the map data 160 can provide informationregarding: the identity and location of different roadways, roadsegments, buildings, or other items or objects (e.g., lampposts,crosswalks and/or curb); the location and directions of traffic lanes(e.g., the location and direction of a parking lane, a turning lane, abicycle lane, or other lanes within a particular roadway or other travelway and/or one or more boundary markings associated therewith); trafficcontrol data (e.g., the location and instructions of signage, trafficlights, and/or other traffic control devices); obstruction information(e.g., temporary or permanent blockages, etc.); event data (e.g., roadclosures/traffic rule alterations due to parades, concerts, sportingevents, etc.); nominal vehicle path data (e.g., indicate of an idealvehicle path such as along the center of a certain lane, etc.); and/orany other map data that provides information that assists the vehiclecomputing system 110 in processing, analyzing, and perceiving itssurrounding environment and its relationship thereto. In someimplementations, the map data 160 can include high definition map data.In some implementations, the map data 160 can include sparse map dataindicative of a limited number of environmental features (e.g., laneboundaries, etc.). In some implementations, the map data can be limitedto geographic area(s) and/or operating domains in which the vehicle 105(or autonomous vehicles generally) may travel (e.g., due tolegal/regulatory constraints, autonomy capabilities, and/or otherfactors).

The vehicle 105 can include a positioning system 165. The positioningsystem 165 can determine a current position of the vehicle 105. This canhelp the vehicle 105 localize itself within its environment. Thepositioning system 165 can be any device or circuitry for analyzing theposition of the vehicle 105. For example, the positioning system 165 candetermine position by using one or more of inertial sensors (e.g.,inertial measurement unit(s), etc.), a satellite positioning system,based on IP address, by using triangulation and/or proximity to networkaccess points or other network components (e.g., cellular towers, WiFiaccess points, etc.) and/or other suitable techniques. The position ofthe vehicle 105 can be used by various systems of the vehicle computingsystem 110 and/or provided to a remote computing system. For example,the map data 160 can provide the vehicle 105 relative positions of theelements of a surrounding environment of the vehicle 105. The vehicle105 can identify its position within the surrounding environment (e.g.,across six axes, etc.) based at least in part on the map data 160. Forexample, the vehicle computing system 110 can process the sensor data155 (e.g., LIDAR data, camera data, etc.) to match it to a map of thesurrounding environment to get an understanding of the vehicle'sposition within that environment. Data indicative of the vehicle'sposition can be stored, communicated to, and/or otherwise obtained bythe autonomy computing system 140.

The autonomy computing system 140 can perform various functions forautonomously operating the vehicle 105. For example, the autonomycomputing system 140 can perform the following functions: perception170A, prediction 170B, and motion planning 170C. For example, theautonomy computing system 140 can obtain the sensor data 155 via thesensor(s) 135, process the sensor data 155 (and/or other data) toperceive its surrounding environment, predict the motion of objectswithin the surrounding environment, and generate an appropriate motionplan through such surrounding environment. In some implementations,these autonomy functions can be performed by one or more sub-systemssuch as, for example, a perception system, a prediction system, a motionplanning system, and/or other systems that cooperate to perceive thesurrounding environment of the vehicle 105 and determine a motion planfor controlling the motion of the vehicle 105 accordingly. In someimplementations, one or more of the perception, prediction, and/ormotion planning functions 170A, 170B, 170C can be performed by (and/orcombined into) the same system and/or via shared computing resources. Insome implementations, one or more of these functions can be performedvia difference sub-systems. As further described herein, the autonomycomputing system 140 can communicate with the one or more vehiclecontrol systems 150 to operate the vehicle 105 according to the motionplan (e.g., via the vehicle interface 145, etc.).

The vehicle computing system 110 (e.g., the autonomy computing system140) can identify one or more objects that within the surroundingenvironment of the vehicle 105 based at least in part on the sensor data155 and/or the map data 160. The objects perceived within thesurrounding environment can be those within the field of view of thesensor(s) 135 and/or predicted to be occluded from the sensor(s) 135.This can include object(s) not in motion or not predicted to move(static objects) and/or object(s) in motion or predicted to be in motion(dynamic objects/actors). The vehicle computing system 110 (e.g.,performing the perception function 170C, using a perception system,etc.) can process the sensor data 155, the map data 160, etc. to obtainperception data 175A. The vehicle computing system 110 can generateperception data 175A that is indicative of one or more states (e.g.,current and/or past state(s)) of one or more objects that are within asurrounding environment of the vehicle 105. For example, the perceptiondata 175A for each object can describe (e.g., for a given time, timeperiod) an estimate of the object's: current and/or past location (alsoreferred to as position); current and/or past speed/velocity; currentand/or past acceleration; current and/or past heading; current and/orpast orientation; size/footprint (e.g., as represented by a boundingshape, object highlighting, etc.); class (e.g., pedestrian class vs.vehicle class vs. bicycle class, etc.), the uncertainties associatedtherewith, and/or other state information. The vehicle computing system110 can utilize one or more algorithms and/or machine-learned model(s)that are configured to identify object(s) based at least in part on thesensor data 155. This can include, for example, one or more neuralnetworks trained to identify object(s) within the surroundingenvironment of the vehicle 105 and the state data associated therewith.The perception data 175A can be utilized for the prediction function175B of the autonomy computing system 140.

The vehicle computing system 110 can be configured to predict a motionof the object(s) within the surrounding environment of the vehicle 105.For instance, the vehicle computing system 110 can generate predictiondata 175B associated with such object(s). The prediction data 175B canbe indicative of one or more predicted future locations of eachrespective object. For example, the prediction system 175B can determinea predicted motion trajectory along which a respective object ispredicted to travel over time. A predicted motion trajectory can beindicative of a path that the object is predicted to traverse and anassociated timing with which the object is predicted to travel along thepath. The predicted path can include and/or be made up of a plurality ofway points. In some implementations, the prediction data 175B can beindicative of the speed and/or acceleration at which the respectiveobject is predicted to travel along its associated predicted motiontrajectory. The vehicle computing system 110 can utilize one or morealgorithms and/or machine-learned model(s) that are configured topredict the future motion of object(s) based at least in part on thesensor data 155, the perception data 175A, map data 160, and/or otherdata. This can include, for example, one or more neural networks trainedto predict the motion of the object(s) within the surroundingenvironment of the vehicle 105 based at least in part on the past and/orcurrent state(s) of those objects as well as the environment in whichthe objects are located (e.g., the lane boundary in which it istravelling, etc.). The prediction data 175B can be utilized for themotion planning function 170C of the autonomy computing system 140.

The vehicle computing system 110 can determine a motion plan for thevehicle 105 based at least in part on the perception data 175A, theprediction data 175B, and/or other data. For example, the vehiclecomputing system 110 can generate motion planning data 175C indicativeof a motion plan. The motion plan can include vehicle actions (e.g.,speed(s), acceleration(s), other actions, etc.) with respect to one ormore of the objects within the surrounding environment of the vehicle105 as well as the objects' predicted movements. The motion plan caninclude one or more vehicle motion trajectories that indicate a path forthe vehicle 105 to follow. A vehicle motion trajectory can be of acertain length and/or time range. A vehicle motion trajectory can bedefined by one or more way points (with associated coordinates). Theplanned vehicle motion trajectories can indicate the path the vehicle105 is to follow as it traverses a route from one location to another.Thus, the vehicle computing system 110 can take into account aroute/route data when performing the motion planning function 170C.

The motion planning system 170C can implement an optimization algorithm,machine-learned model, etc. that considers cost data associated with avehicle action as well as other objective functions (e.g., costfunctions based on speed limits, traffic lights, etc.), if any, todetermine optimized variables that make up the motion plan. The vehiclecomputing system 110 can determine that the vehicle 105 can perform acertain action (e.g., pass an object, etc.) without increasing thepotential risk to the vehicle 105 and/or violating any traffic laws(e.g., speed limits, lane boundaries, signage, etc.). For instance, thevehicle computing system 110 can evaluate the predicted motiontrajectories of one or more objects during its cost data analysis tohelp determine an optimized vehicle trajectory through the surroundingenvironment. The motion planning system 170C can generate cost dataassociated with such trajectories. In some implementations, one or moreof the predicted motion trajectories and/or perceived objects may notultimately change the motion of the vehicle 105 (e.g., due to anoverriding factor). In some implementations, the motion plan may definethe vehicle's motion such that the vehicle 105 avoids the object(s),reduces speed to give more leeway to one or more of the object(s),proceeds cautiously, performs a stopping action, passes an object,queues behind/in front of an object, etc.

The vehicle computing system 110 can be configured to continuouslyupdate the vehicle's motion plan and a corresponding planned vehiclemotion trajectories. For example, in some implementations, the vehiclecomputing system 110 can generate new motion planning data 175C/motionplan(s) for the vehicle 105 (e.g., multiple times per second, etc.).Each new motion plan can describe a motion of the vehicle 105 over thenext planning period (e.g., next several seconds, etc.). Moreover, a newmotion plan may include a new planned vehicle motion trajectory. Thus,in some implementations, the vehicle computing system 110 cancontinuously operate to revise or otherwise generate a short-term motionplan based on the currently available data. Once the optimizationplanner has identified the optimal motion plan (or some other iterativebreak occurs), the optimal motion plan (and the planned motiontrajectory) can be selected and executed by the vehicle 105.

The vehicle computing system 110 can cause the vehicle 105 to initiate amotion control in accordance with at least a portion of the motionplanning data 175C. A motion control can be an operation, action, etc.that is associated with controlling the motion of the vehicle 105. Forinstance, the motion planning data 175C can be provided to the vehiclecontrol system(s) 150 of the vehicle 105. The vehicle control system(s)150 can be associated with a vehicle interface 145 that is configured toimplement a motion plan. The vehicle interface 145 can serve as aninterface/conduit between the autonomy computing system 140 and thevehicle control systems 150 of the vehicle 105 and anyelectrical/mechanical controllers associated therewith. The vehicleinterface 145 can, for example, translate a motion plan intoinstructions for the appropriate vehicle control component (e.g.,acceleration control, brake control, steering control, etc.). By way ofexample, the vehicle interface 145 can translate a determined motionplan into instructions to adjust the steering of the vehicle 105 “X”degrees, apply a certain magnitude of braking force, increase/decreasespeed, etc. The vehicle interface 145 can help facilitate theresponsible vehicle control (e.g., braking control system, steeringcontrol system, acceleration control system, etc.) to execute theinstructions and implement a motion plan (e.g., by sending controlsignal(s), making the translated plan available, etc.). This can allowthe vehicle 105 to autonomously travel within the vehicle's surroundingenvironment.

The vehicle computing system 110 can store other types of data. Forexample, an indication, record, and/or other data indicative of thestate of the vehicle (e.g., its location, motion trajectory, healthinformation, etc.), the state of one or more users (e.g., passengers,operators, etc.) of the vehicle, and/or the state of an environmentincluding one or more objects (e.g., the physical dimensions and/orappearance of the one or more objects, locations, predicted motion,etc.) can be stored locally in one or more memory devices of the vehicle105. Additionally, the vehicle 105 can communicate data indicative ofthe state of the vehicle, the state of one or more passengers of thevehicle, and/or the state of an environment to a computing system thatis remote from the vehicle 105, which can store such information in oneor more memories remote from the vehicle 105. Moreover, the vehicle 105can provide any of the data created and/or store onboard the vehicle 105to another vehicle.

The vehicle computing system 110 can include the one or more vehicleuser devices 180. For example, the vehicle computing system 110 caninclude one or more user devices with one or more display deviceslocated onboard the vehicle 15. A display device (e.g., screen of atablet, laptop, and/or smartphone) can be viewable by a user of thevehicle 105 that is located in the front of the vehicle 105 (e.g.,driver's seat, front passenger seat). Additionally, or alternatively, adisplay device can be viewable by a user of the vehicle 105 that islocated in the rear of the vehicle 105 (e.g., a back passenger seat).The user device(s) associated with the display devices can be any typeof user device such as, for example, a table, mobile phone, laptop, etc.The vehicle user device(s) 180 can be configured to function ashuman-machine interfaces. For example, the vehicle user device(s) 180can be configured to obtain user input, which can then be utilized bythe vehicle computing system 110 and/or another computing system (e.g.,a remote computing system, etc.). For example, a user (e.g., a passengerfor transportation service, a vehicle operator, etc.) of the vehicle 105can provide user input to adjust a destination location of the vehicle105. The vehicle computing system 110 and/or another computing systemcan update the destination location of the vehicle 105 and the routeassociated therewith to reflect the change indicated by the user input.

The vehicle 105 can be configured to perform vehicle services for one ora plurality of different service entities 185. A vehicle 105 can performa vehicle service by, for example and as further described herein,travelling (e.g., traveling autonomously) to a location associated witha requested vehicle service, allowing user(s) and/or item(s) to board orotherwise enter the vehicle 105, transporting the user(s) and/oritem(s), allowing the user(s) and/or item(s) to deboard or otherwiseexit the vehicle 105, etc. In this way, the vehicle 105 can provide thevehicle service(s) for a service entity to a user.

A service entity 185 can be associated with the provision of one or morevehicle services. For example, a service entity can be an individual, agroup of individuals, a company (e.g., a business entity, organization,etc.), a group of entities (e.g., affiliated companies), and/or anothertype of entity that offers and/or coordinates the provision of one ormore vehicle services to one or more users. For example, a serviceentity can offer vehicle service(s) to users via one or more softwareapplications (e.g., that are downloaded onto a user computing device),via a website, and/or via other types of interfaces that allow a user torequest a vehicle service. As described herein, the vehicle services caninclude transportation services (e.g., by which a vehicle transportsuser(s) from one location to another), delivery services (e.g., by whicha vehicle transports/delivers item(s) to a requested destinationlocation), courier services (e.g., by which a vehicle retrieves item(s)from a requested origin location and transports/delivers the item to arequested destination location), and/or other types of services. Thevehicle services can be wholly performed by the vehicle 105 (e.g.,travelling from the user/item origin to the ultimate destination, etc.)or performed by one or more vehicles and/or modes of transportation(e.g., transferring the user/item at intermediate transfer points,etc.).

An operations computing system 190A of the service entity 185 can helpto coordinate the performance of vehicle services by autonomousvehicles. The operations computing system 190A can include and/orimplement one or more service platforms of the service entity. Theoperations computing system 190A can include one or more computingdevices. The computing device(s) can include various components forperforming various operations and functions. For instance, the computingdevice(s) can include one or more processors and one or more tangible,non-transitory, computer readable media (e.g., memory devices, etc.).The one or more tangible, non-transitory, computer readable media canstore instructions that when executed by the one or more processorscause the operations computing system 190 (e.g., its one or moreprocessors, etc.) to perform operations and functions, such as thosedescribed herein matching users and vehicles/vehicle fleets, deployingvehicles, facilitating the provision of vehicle services via autonomousvehicles, etc.

A user 125 can request a vehicle service from a service entity 185. Forexample, the user 125 can provide user input to a user device 130 torequest a vehicle service (e.g., via a user interface associated with amobile software application of the service entity 185 running on theuser device 130). The user device 130 can communicate data indicative ofa vehicle service request 195 to the operations computing system 190Aassociated with the service entity 185 (and/or another associatedcomputing system that can then communicate data to the operationscomputing system 190A). The vehicle service request 195 can beassociated with a user. The associated user can be the one that submitsthe vehicle service request (e.g., via an application on the user device130). In some implementations, the user may not be the user that submitsthe vehicle service request. The vehicle service request can beindicative of the user. For example, the vehicle service request caninclude an identifier associated with the user and/or the user'sprofile/account with the service entity 185. The vehicle service request195 can be generated in a manner that avoids the use of personallyidentifiable information and/or allows the user to control the types ofinformation included in the vehicle service request 195. The vehicleservice request 195 can also be generated, communicated, stored, etc. ina secure manner to protect information.

The vehicle service request 195 can indicate various types ofinformation. For example, the vehicle service request 194 can indicatethe type of vehicle service that is desired (e.g., a transportationservice, a delivery service, a courier service, etc.), one or morelocations (e.g., an origin location, a destination location, etc.),timing constraints (e.g., pick-up time, drop-off time, deadlines, etc.),and/or geographic constraints (e.g., to stay within a certain area,etc.). The vehicle service request 195 can indicate a type/size/class ofvehicle such as, for example, a sedan, an SUV, luxury vehicle, standardvehicle, etc. The vehicle service request 195 can indicate a product ofthe service entity 185. For example, the vehicle service request 195 canindicate that the user is requesting a transportation pool product bywhich the user would potentially share the vehicle (and costs) withother users/items. In some implementations, the vehicle service request195 can explicitly request for the vehicle service to be provided by anautonomous vehicle or a human-driven vehicle. In some implementations,the vehicle service request 195 can indicate a number of users that willbe riding in the vehicle/utilizing the vehicle service. In someimplementations, the vehicle service request 195 can indicatepreferences/special accommodations of an associated user (e.g., musicpreferences, climate preferences, wheelchair accessibility, etc.) and/orother information.

The operations computing system 190A of the service entity 185 canprocess the data indicative of the vehicle service request 195 andgenerate a vehicle service assignment that is associated with thevehicle service request. The operations computing system can identifyone or more vehicles that may be able to perform the requested vehicleservices to the user 125. The operations computing system 190A canidentify which modes of transportation are available to a user for therequested vehicle service (e.g., light electric vehicles, human-drivevehicles, autonomous vehicles, aerial vehicle, etc.) and/or the numberof transportation modes/legs of a potential itinerary of the user forcompleting the vehicle service (e.g., single or plurality of modes,single or plurality of legs, etc.). For example, the operationscomputing system 190A can determined which autonomous vehicle(s) areonline with the service entity 185 (e.g., available for a vehicleservice assignment, addressing a vehicle service assignment, etc.) tohelp identify which autonomous vehicle(s) would be able to provide thevehicle service.

The operations computing system 190A and/or the vehicle computing system110 can communicate with one or more other computing systems 190B thatare remote from the vehicle 105. This can include, for example,computing systems associated with government functions (e.g., emergencyservices, regulatory bodies, etc.), computing systems associated withvehicle providers other than the service entity, computing systems ofother vehicles (e.g., other autonomous vehicles, aerial vehicles, etc.).Communication with the other computing systems 190B can occur via thenetwork(s) 120.

In some implementations, the vehicle control systems 150 can beconfigured to control operation of each of a plurality of near-rangecamera assemblies 300 of the vehicle 105. Details of the one or morenear-range camera assemblies 300 will be discussed below in more detail.However, it should be understood that the one or more near-range cameraassemblies 300 can include a LIDAR unit 400 and a camera 500. Thevehicle control systems 150 can be configured to control operation ofone or more cleaning systems 302 associated with each of the pluralityof near-range camera assemblies 300. For instance, in someimplementations, the one or more cleaning systems 302 can include an airknife system. Alternatively, or additionally, the vehicle controlsystems 150 can be configured to control operation of a first cleaningsystem associated with cleaning debris from the LIDAR unit 400 of of afirst near-range camera assembly of the plurality of near-range cameraassemblies 300. In addition, the vehicle control systems 150 can beconfigured to control operation of a second cleaning system associatedwith cleaning debris from a lens of the camera 500 of the firstnear-range camera assembly of the plurality of near-range cameraassemblies 300.

In some implementations, the first cleaning system and the secondcleaning system can operate concurrently. In this manner, debris can becleaned from the housing of the LIDAR unit and the lens of the camera atthe same time. In alternative implementations, the first cleaning systemand the second cleaning system can be operated at different times. Forinstance, in some implementations, the first cleaning system can beoperated to clean debris from the housing of the LIDAR unit when a speedof the vehicle is greater than a first threshold value (e.g., 5 MPH) andless than a second threshold value (e.g., 20 MPH). Conversely, thesecond cleaning system can be operated when the speed of the vehicle isless than the first threshold value. For instance, the second cleaningsystem can be operated to clean debris from the lens of the camera whenthe autonomous vehicle is idle (e.g., not moving) at an intersection.Furthermore, in some implementations, operation of the first cleaningsystem and the second cleaning system can be prohibited when the speedof the autonomous vehicle is above the second threshold value. Forexample, operation of the first cleaning system and the second cleaningsystem when the autonomous vehicle is traversing a highway orinterstate.

Referring now to FIGS. 2 and 3, the vehicle 105 can include a vehiclebody 200. It should be understood that the vehicle body 200 can includea frame and/or body trim panels (e.g., front bumper, rear bumper, etc.).As shown, the plurality of near-range camera assemblies 300 coupled tothe vehicle body 200. For instance, in some implementations, the vehicle105 can include a first near-range camera assembly 310, a secondnear-range camera assembly 312, a third near-range camera assembly 320,and a fourth near-range camera assembly 322. In alternativeimplementations, the vehicle 105 can include more or fewer near-rangecamera assemblies 300.

As shown, the first near-range camera assembly 310 and the secondnear-range camera assembly 312 can be coupled to opposing ends (e.g.,front end and rear end) of the vehicle body 200. For instance, the firstnear-range camera assembly 310 can be coupled to a front bumper 210 ofthe vehicle body 200. More particularly, the first near-range cameraassembly 310 can be coupled to a front grille 212 of the front bumper210. Conversely, the second near-range camera assembly 312 can becoupled to a rear bumper of the vehicle body 200. As shown, the thirdnear-range camera assembly 320 and the fourth near-range camera assembly322 can be coupled to opposing sides of the vehicle body 200.

Referring now to FIG. 4, a front view of the first near-range cameraassembly 310 and the second near-range camera assembly 312 is providedaccording to example embodiments of the present disclosure. As shown,the first near-range camera assembly 310 and the second near-rangecamera assembly 312 can each include a LIDAR unit 400. The LIDAR unit400 can include a housing 410. The LIDAR unit 400 can further includeone or more emitters 420 disposed within the housing 410 and one or morereceivers 430 disposed within the housing 410. The one or more emitters420 can include a laser source configured to emit a laser beam. The oneor more receivers 430 can include a detector (e.g., photodetector)configured to detect a reflected laser beam. The first near-range cameraassembly 310 and the second near-range camera assembly 312 can furtherinclude a camera 500 having a camera lens 502. In some implementations,the camera 500 can have a field-of-view of about 360 degrees. Forinstance, in some implementations, the camera lens 502 can be a fisheyelens.

Referring now to FIGS. 5-7, the first near-range camera assembly 310 andthe second near-range camera assembly 312 can each include a sensorplatform or mounting assembly 600 to facilitate coupling the LIDAR unit400 (FIG. 4) and the camera 500 to the vehicle body 200 (FIG. 2). Asshown, the mounting assembly 600 can include a LIDAR mount 610 and acamera mount or camera housing 620. The LIDAR mount 610 can beconfigured to accommodate the LIDAR unit 400 (FIG. 4). The camerahousing 620 can be configured to accommodate the camera 500.

In some implementations, the LIDAR mount 610 can include one or moreLIDAR brackets 630 to facilitate coupling the mounting assembly 600 tothe vehicle body 200 (FIG. 3) of the vehicle 105 (FIG. 2). For instance,in some implementations, the one or more LIDAR brackets 630 can becountable to one or more additional components (e.g., brackets). Inalternative implementations, the LIDAR bracket 630 can be directlymounted to the vehicle body 200 of the vehicle 105 without the one ormore additional components. It should be understood that the one or moreLIDAR brackets 630 define a plurality of apertures 632 to accommodatemechanical fasteners (e.g., bolts, screw, etc.) used to couple the LIDARmount 610 to the vehicle body 200 (FIG. 2) or the one or more additionalcomponents.

The LIDAR unit 400 (FIG. 4) can be coupled to the LIDAR mount 610 suchthat the LIDAR unit 400 is positioned on a first surface 612 of theLIDAR mount 610. The camera housing 620 can be coupled to the LIDARmount 610 such that the camera housing 620 is positioned on a secondsurface 614 of the LIDAR mount 610. As shown, the second surface 614 canbe opposite the first surface 612. In some implementations the LIDARmount 610 can define a first plurality of apertures 616 to accommodatemechanical fasteners (e.g., bolts, screws, etc.) associated withcoupling the LIDAR unit 400 to the LIDAR mount 610 such that the LIDARunit 400 is positioned on the first surface 612 of the LIDAR mount 610.Additionally, the LIDAR mount 610 can define a second plurality ofapertures 618 to accommodate mechanical fasteners (e.g., bolts, screws,etc.) associated with coupling the camera housing 620 to the LIDAR mount610 such that the camera housing 620 is positioned on the second surface614 of the LIDAR mount 610. It should be understood that the firstplurality of apertures 616 and the second plurality of apertures 618extend through the first surface 612 of the LIDAR mount 610 and thesecond surface 614 of the LIDAR mount 610.

As shown, the camera housing 620 can be removably coupled to the LIDARmount 610 such that the camera 500 is oriented at an acute angle 700relative to the LIDAR mount 610 when the camera 500 is positioned withinthe camera housing 620. In this manner, a field of view of the camera500 can include a larger portion of objects (e.g., pedestrian, bicycle)in close proximity (e.g., less than 10 meters, less than 5 meters, etc.)to the vehicle 105 (FIG. 2). For instance, in some implementations, theacute angle 700 can be less than about 20 degrees. Furthermore, in someimplementations, the field of view of the camera 500 can intersect afield of view of the LIDAR unit 400 due, at least in part, to the camera500 being tilted relative to the LIDAR mount 610 at the acute angle 700.

Referring now to FIGS. 8 and 9, the first near-range camera assembly 310(FIG. 4) and the second near-range camera assembly 312 (FIG. 4) can eachinclude a cover 800 to hide the mounting assembly 600 (FIG. 5) fromview. As shown, the cover 800 can define a first opening 810 toaccommodate housing 410 (FIG. 4) of the LIDAR unit 400 (FIG. 4). Inaddition, the cover 800 can define a second opening 820 to accommodatethe camera housing 620. For instance, a portion (e.g., lens) of thecamera 500 can be positioned within the second opening 820 when thecamera 500 is positioned within the camera housing 620 and the cover 800is positioned over the mounting assembly 600.

In some implementations, the cover 800 can include a backboard 830 and ahoop 840 defining the first opening 810 to accommodate the LIDAR unit400. In this manner, the shape of the first opening 810 can correspondto the shape of the housing 410 (FIG. 4) of the LIDAR unit 400. Moreparticularly, the backboard 830 and the hoop can contact the housing 410of the LIDAR unit 400 to prevent debris (e.g., rocks, dirt, etc.) fromaccumulating on the LIDAR mount 610 (FIG. 5).

In some implementations, the first near-range camera assembly 310 andthe second near-range camera assembly 312 can each include a retentionplate 622 positioned to retain the camera 500 within a cavity defined bythe camera housing 620. In addition, the first near-range cameraassembly 310 and the second near-range camera assembly 312 can eachinclude a spring plate 624 positioned between the retention plate 622and the camera housing 620 such that the spring plate 624 exerts a forceon the camera 500. In this manner, the camera 500 can be retained withinthe camera housing 620.

Referring now to FIG. 10, the third near-range camera assembly 320 andthe fourth-near-range camera assembly 322 are provided according toexample embodiments of the present disclosure. The third near-rangecamera assembly 320 and the fourth near-range camera assembly 322 can beconfigured in substantially the same manner as the first near-rangecamera assembly 310 and the second near-range camera assembly 312discussed above with reference to FIGS. 3-9. For instance, the thirdnear-range camera assembly 320 and the second near-range camera assembly322 can each include the LIDAR unit 400. Furthermore, the thirdnear-range camera assembly 320 and the second near-range camera assembly322 can each include the camera 500 and the camera housing 620. Itshould be understood that the LIDAR unit 400 and the camera housing 620of the third and fourth near-range camera assemblies 320, 322 are eachcoupled to a LIDAR mount. Furthermore, it should be understood that thecamera housing 620 is coupled to the LIDAR mount such that the camera istilted relative to the LIDAR mount at an acute angle (e.g., less than 20degrees). In this manner, a field of view associated with the camera 500can intersect with a field of view associated with the LIDAR unit 400.

Referring now to FIG. 11, a block diagram of a LIDAR system 900 isprovided according to example embodiments of the present disclosure. Itshould be understood that the LIDAR system 900 can be included as partof the sensors 135 discussed above with reference to FIG. 1. As shown,the LIDAR system 900 can include multiple channels 910; specifically,channels 1-N are illustrated. It should be understood that channels 1-Ncan be included in a single LIDAR unit 400 or may be spread acrossmultiple LIDAR units 400. Each channel 910 can output point data thatprovides a single point of ranging information. The point data output byeach of the channels 910 (e.g., point data_(1-N)) can be combined tocreate a point cloud that corresponds to a three-dimensionalrepresentation of the surrounding environment.

As shown, each channel 910 can include an emitter 920 paired with areceiver 930. The emitter 920 emits a laser signal into the environmentthat is reflected off the surrounding environment and returned back to adetector 932 (e.g., an optical detector) of the receiver 930. Eachemitter 920 can have an adjustable power level that controls anintensity of the emitted laser signal. The adjustable power level allowsthe emitter 920 to be capable of emitting the laser signal at one ofmultiple different power levels (e.g., intensities).

The detector 932 can provide the return signal to a read-out circuit434. The read-out circuit 934 can, in turn, output the point data basedon the return signal. The point data can indicate a distance the LIDARsystem 900 is from a detected object (e.g., road, pedestrian, vehicle,etc.) that is determined by the read-out circuit 934 by measuringtime-of-flight (ToF), which is the time elapsed time between the emitter920 emitting the laser signal and the receiver 930 detecting the returnsignal.

The point data further includes an intensity value corresponding to eachreturn signal. The intensity value indicates a measure of intensity ofthe return signal determined by the read-out circuit 934. As notedabove, the intensity of the return signal provides information about thesurface reflecting the signal and can be used by the autonomy computingsystem 140 (FIG. 1) for localization, perception, prediction, and/ormotion planning. The intensity of the return signals depends on a numberof factors, such as the distance of the LIDAR system 900 to the detectedobject, the angle of incidence at which the emitter 920 emits the lasersignal, temperature of the surrounding environment, the alignment of theemitter 920 and the receiver 930, and the reflectivity of the detectedsurface.

As shown, a reflectivity processing system 940 receives the point datafrom the LIDAR system 900 and processes the point data to classifyspecular reflectivity characteristics of objects. The reflectivityprocessing system 940 classifies the specular reflectivitycharacteristics of objects based on a comparison of reflectivity valuesderived from intensity values of return signals. In some embodiments,the LIDAR system 900 can be calibrated to produce the reflectivityvalues. For example, the read-out circuit 934 or another component ofthe LIDAR system 900 can be configured to normalize the intensity valuesto produce the reflectivity values. In these embodiments, thereflectivity values may be included in the point data received by thereflectivity processing system 940 from the LIDAR system 900. In otherembodiments, the reflectivity processing system 940 may generate thereflectivity values based on intensity return values included in thepoint data received from the LIDAR system 900.

Regardless of which component is responsible for generating thereflectivity values, the process for doing so may, in some embodiments,include using a linear model to compute one or more calibrationmultipliers and one or more bias values to be applied to returnintensity values. Depending on the embodiment, a calibration multiplierand bias value may be computed for and applied to each channel of theLIDAR system 900 at each power level. The linear model assumes a uniformdiffuse reflectivity for all surfaces and describes an expectedintensity value as a function of a raw intensity variable, a calibrationmultiplier variable, and/or a bias variable. The computing of thecalibration multiplier and bias value for each channel/power levelcombination includes determining a median intensity value based on theraw intensity values output by the channel at the power level and usingthe median intensity value as the expected intensity value in the linearmodel while optimizing values for the calibration multiplier variableand bias variable. As an example, the calibration multiplier and biasvalue may be computed by solving the linear model using an IteratedRe-weighted Least Squares approach.

The calibration multiplier and bias value computed for each channel 910at each power level can be assigned to the corresponding channel/powerlevel combination. In this way, each power level of each channel of theLIDAR system 900 can have an independently assigned calibrationmultiplier and bias value from which reflectivity values may be derived.Once assigned, the calibration multiplier and bias value of eachchannel/power level combination can be used at run-time to determinereflectivity values from subsequent intensity values produced by thecorresponding channel at the corresponding power level during operationof an autonomous or semi-autonomous vehicle. More specifically,reflectivity values can be determined from the linear model by using thevalue of the calibration multiplier and the bias value for thecalibration multiplier variable and bias variable, respectively. In thismanner, the intensity values can be normalized to be more aligned withthe reflectivity of a surface by taking into account factors such as thedistance of the LIDAR system 900 to the detected surface, the angle ofincidence at which the emitter 920 emits the laser signal, temperatureof the surrounding environment, and/or the alignment of the emitter 1120and the receiver 930.

While the present subject matter has been described in detail withrespect to specific example embodiments and methods thereof, it will beappreciated that those skilled in the art, upon attaining anunderstanding of the foregoing can readily produce alterations to,variations of, and equivalents to such embodiments. Accordingly, thescope of the present disclosure is by way of example rather than by wayof limitation, and the subject disclosure does not preclude inclusion ofsuch modifications, variations and/or additions to the present subjectmatter as would be readily apparent to one of ordinary skill in the art.

1. A sensor platform for mounting a light detection and ranging (Lidar)unit and a camera to an autonomous vehicle, the sensor platformcomprising: a Lidar mount couplable to an autonomous vehicle, the mountcomprising a first surface and a second surface, the second surfacebeing different from the first surface; and a camera mount couplable tothe Lidar mount, wherein when the Lidar unit is coupled to the Lidarmount, the Lidar unit is coupled to the first surface of the Lidarmount, and wherein when the camera mount is coupled to the Lidar mount,the camera mount is coupled to the second surface of the Lidar mount. 2.The sensor platform of claim 1, wherein the camera mount defines anopening configured to accommodate a lens of the camera.
 3. The sensorplatform of claim 1, further comprising: a cover defining a firstopening and a second opening, the cover removably coupled to the Lidarmount such that a portion of the Lidar unit is positioned within thefirst opening when the Lidar unit is coupled to the Lidar mount and aportion of the camera housing is positioned within the second openingwhen the camera mount is coupled to the Lidar mount.
 4. The sensorplatform of claim 1, wherein the Lidar mount defines a first apertureand a second aperture, the first aperture configured to accommodate afastener associated with coupling the Lidar unit to the first surface ofthe Lidar mount, the second aperture configured to accommodate afastener associated with coupling the camera mount to the second surfaceof the Lidar mount.
 5. The sensor platform of claim 1, furthercomprising: a spring plate positioned to retain the camera within acavity defined by the camera housing.
 6. The sensor platform of claim 1,wherein the Lidar mount is couplable to a grille of the autonomousvehicle.
 7. The sensor platform of claim 1, wherein: when the Lidar unitis coupled to the Lidar mount, the Lidar unit contacts the first surfaceof the Lidar mount, and when the camera mount is coupled to the Lidarmount, the camera mount contacts the second surface of the Lidar mount.8. The sensor platform of claim 1, wherein when the camera mount iscoupled to the Lidar mount and the camera is positioned within a cavitydefined by the camera mount, the camera is tilted at an acute anglerelative to the Lidar mount. 9-16. (canceled)
 17. An autonomous vehiclecomprising: a vehicle body; and a plurality of camera assembliesrespectively coupled to the vehicle body at different locations, atleast one camera assembly of the plurality of camera assembliescomprising: a sensor platform comprising a light detection and ranging(Lidar) mount and a camera mount, the Lidar mount coupled to the vehiclebody, the Lidar mount comprising a first surface and a second surfacethat is different from the first surface, the camera mount coupled tothe second surface of the Lidar mount, the camera mount defining acavity; a Lidar unit coupled to the first surface of the Lidar mount;and a camera positioned within the cavity defined by the camera mount.18. The autonomous vehicle of claim 17, wherein the plurality of cameraassemblies comprise: a first camera assembly coupled to a front bumperof the vehicle body; and a second camera assembly coupled to a rearbumper of the vehicle body.
 19. The autonomous vehicle of claim 18,wherein the plurality of camera assemblies further comprise: a thirdcamera assembly coupled to a first door frame of the autonomous vehicle;and a fourth camera assembly coupled to a second door frame of theautonomous vehicle, the second door frame spaced apart from the firstdoor frame along a lateral direction of the vehicle body.
 20. Theautonomous vehicle of claim 17, further comprising: a first cleaningsystem configured to clean debris from a housing of the Lidar unit; anda second cleaning system configured to clean debris from a lens of thecamera.
 21. The autonomous vehicle of claim 17, wherein the camera mountdefines an opening configured to accommodate a lens of the camera. 22.The autonomous vehicle of claim 17, wherein the at least one cameraassembly further comprises: a retention plate positioned to retain thecamera within the cavity defined by the camera mount; and a spring platepositioned between the camera mount and the retention plate.
 23. Theautonomous vehicle of claim 17, wherein the at least one camera assemblyfurther comprises: a cover defining a first opening and a secondopening, the cover removably coupled to the Lidar mount such that aportion of the Lidar unit is positioned within the first opening whenthe Lidar unit is coupled to the Lidar mount and a portion of the camerahousing is positioned within the second opening when the camera mount iscoupled to the Lidar mount.
 24. The autonomous vehicle of claim 17,wherein the Lidar mount defines a first aperture and a second aperture,the first aperture configured to accommodate a fastener associated withcoupling the Lidar unit to the first surface of the Lidar mount, thesecond aperture configured to accommodate a fastener associated withcoupling the camera mount to the second surface of the Lidar mount. 25.The autonomous vehicle of claim 17, wherein: the Lidar unit contacts thefirst surface of the Lidar mount; and the camera mount contacts thesecond surface of the Lidar mount.
 26. The autonomous vehicle of claim7, wherein the camera is tilted at an acute angle relative to the Lidarmount.
 27. The autonomous vehicle of claim 17, wherein the camera has a360 degree field of view.
 28. An autonomous vehicle control systemcomprising: a sensor platform comprising a Lidar mount and a cameramount, the Lidar mount couplable to an autonomous vehicle; the Lidarmount comprising a first surface and a second surface that is differentthan the first surface, the camera mount coupled to the second surfaceof the Lidar mount, the camera mount defining a cavity; a Lidar unitcoupled to the first surface of the Lidar mount; and a camera positionedwithin the cavity defined by the camera mount.